Supervised Learning
Explore the Iris dataset, train a neural network classifier, evaluate its performance with a confusion matrix and per-class metrics, then test it with your own custom inputs in the prediction playground.
Learn Supervised Learning on DataCamp
Curated courses and career tracks to take your understanding from this demo to real-world mastery. All links open directly on DataCamp.
Supervised Learning with scikit-learn
Learn how to build and tune predictive models using scikit-learn. Cover classification, regression, model evaluation, and hyperparameter tuning.
Linear Classifiers in Python
Understand logistic regression and SVMs. Learn gradient descent optimization and how hyperplanes separate classes.
Machine Learning with Tree-Based Models in Python
Learn decision trees, bagging, random forests, boosting, and gradient boosting to solve classification and regression tasks.
Model Validation in Python
Learn best practices for validating machine learning models including cross-validation, bias-variance tradeoff, and hyperparameter tuning.
Preprocessing for Machine Learning in Python
Learn how to prepare data for machine learning models—handling missing values, encoding categories, and scaling features.
Machine Learning Scientist with Python
Gain expertise in supervised learning, deep learning, big data, and more with this comprehensive career track.
